Overview

Brought to you by YData

Dataset statistics

Number of variables31
Number of observations140000
Missing cells1624169
Missing cells (%)37.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory32.2 MiB
Average record size in memory241.0 B

Variable types

Numeric21
Categorical4
Boolean4
Unsupported1
Text1

Alerts

alcohol_use is highly overall correlated with cigarette_use and 3 other fieldsHigh correlation
apgar_1min is highly overall correlated with child_race and 3 other fieldsHigh correlation
apgar_5min is highly overall correlated with record_weightHigh correlation
born_alive_alive is highly overall correlated with ever_bornHigh correlation
child_race is highly overall correlated with apgar_1min and 2 other fieldsHigh correlation
cigarette_use is highly overall correlated with alcohol_use and 3 other fieldsHigh correlation
cigarettes_per_day is highly overall correlated with alcohol_use and 2 other fieldsHigh correlation
drinks_per_week is highly overall correlated with alcohol_use and 2 other fieldsHigh correlation
ever_born is highly overall correlated with born_alive_aliveHigh correlation
father_age is highly overall correlated with mother_marriedHigh correlation
father_race is highly overall correlated with mother_raceHigh correlation
mother_married is highly overall correlated with father_ageHigh correlation
mother_race is highly overall correlated with father_raceHigh correlation
mother_residence_state is highly overall correlated with stateHigh correlation
record_weight is highly overall correlated with alcohol_use and 9 other fieldsHigh correlation
source_year is highly overall correlated with apgar_1min and 3 other fieldsHigh correlation
state is highly overall correlated with mother_residence_stateHigh correlation
wday is highly overall correlated with record_weightHigh correlation
weight_gain_pounds is highly overall correlated with record_weightHigh correlation
year is highly overall correlated with apgar_1min and 3 other fieldsHigh correlation
state is highly imbalanced (55.9%)Imbalance
plurality is highly imbalanced (90.7%)Imbalance
mother_residence_state is highly imbalanced (80.9%)Imbalance
cigarette_use is highly imbalanced (53.5%)Imbalance
alcohol_use is highly imbalanced (56.4%)Imbalance
record_weight is highly imbalanced (89.1%)Imbalance
day has 131346 (93.8%) missing valuesMissing
wday has 8654 (6.2%) missing valuesMissing
state has 123104 (87.9%) missing valuesMissing
child_race has 124068 (88.6%) missing valuesMissing
apgar_1min has 127032 (90.7%) missing valuesMissing
apgar_5min has 11933 (8.5%) missing valuesMissing
mother_residence_state has 123104 (87.9%) missing valuesMissing
mother_race has 64325 (45.9%) missing valuesMissing
gestation_weeks has 2301 (1.6%) missing valuesMissing
mother_birth_state has 123747 (88.4%) missing valuesMissing
cigarette_use has 81576 (58.3%) missing valuesMissing
cigarettes_per_day has 134232 (95.9%) missing valuesMissing
alcohol_use has 74321 (53.1%) missing valuesMissing
drinks_per_week has 136403 (97.4%) missing valuesMissing
weight_gain_pounds has 11178 (8.0%) missing valuesMissing
born_alive_alive has 93590 (66.8%) missing valuesMissing
born_alive_dead has 93623 (66.9%) missing valuesMissing
born_dead has 93652 (66.9%) missing valuesMissing
father_race has 64325 (45.9%) missing valuesMissing
apgar_5min is highly skewed (γ1 = 29.33223705)Skewed
born_alive_dead is highly skewed (γ1 = 42.83459027)Skewed
born_dead is highly skewed (γ1 = 32.97234222)Skewed
lmp is an unsupported type, check if it needs cleaning or further analysisUnsupported
drinks_per_week has 3335 (2.4%) zerosZeros
born_alive_alive has 18875 (13.5%) zerosZeros
born_alive_dead has 45519 (32.5%) zerosZeros
born_dead has 36177 (25.8%) zerosZeros

Reproduction

Analysis started2024-07-30 18:00:03.123913
Analysis finished2024-07-30 18:02:26.682203
Duration2 minutes and 23.56 seconds
Software versionydata-profiling vv4.9.0
Download configurationconfig.json

Variables

source_year
Real number (ℝ)

HIGH CORRELATION 

Distinct40
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2004.2591
Minimum1969
Maximum2008
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2024-07-30T18:02:26.896881image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum1969
5-th percentile1985
Q12005
median2006
Q32007
95-th percentile2008
Maximum2008
Range39
Interquartile range (IQR)2

Descriptive statistics

Standard deviation7.0601516
Coefficient of variation (CV)0.0035225742
Kurtosis9.4412916
Mean2004.2591
Median Absolute Deviation (MAD)1
Skewness-3.1472445
Sum2.8059628 × 108
Variance49.84574
MonotonicityNot monotonic
2024-07-30T18:02:27.334414image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
2007 31305
22.4%
2006 31118
22.2%
2008 31025
22.2%
2005 29656
21.2%
1997 548
 
0.4%
1991 543
 
0.4%
1998 543
 
0.4%
1990 540
 
0.4%
1988 539
 
0.4%
1980 535
 
0.4%
Other values (30) 13648
9.7%
ValueCountFrequency (%)
1969 380
0.3%
1970 402
0.3%
1971 276
0.2%
1972 236
0.2%
1973 232
0.2%
1974 234
0.2%
1975 244
0.2%
1976 421
0.3%
1977 532
0.4%
1978 519
0.4%
ValueCountFrequency (%)
2008 31025
22.2%
2007 31305
22.4%
2006 31118
22.2%
2005 29656
21.2%
2004 505
 
0.4%
2003 459
 
0.3%
2002 491
 
0.4%
2001 491
 
0.4%
2000 520
 
0.4%
1999 508
 
0.4%

year
Real number (ℝ)

HIGH CORRELATION 

Distinct40
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2004.2591
Minimum1969
Maximum2008
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2024-07-30T18:02:27.748667image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum1969
5-th percentile1985
Q12005
median2006
Q32007
95-th percentile2008
Maximum2008
Range39
Interquartile range (IQR)2

Descriptive statistics

Standard deviation7.0601516
Coefficient of variation (CV)0.0035225742
Kurtosis9.4412916
Mean2004.2591
Median Absolute Deviation (MAD)1
Skewness-3.1472445
Sum2.8059628 × 108
Variance49.84574
MonotonicityNot monotonic
2024-07-30T18:02:28.230302image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
2007 31305
22.4%
2006 31118
22.2%
2008 31025
22.2%
2005 29656
21.2%
1997 548
 
0.4%
1991 543
 
0.4%
1998 543
 
0.4%
1990 540
 
0.4%
1988 539
 
0.4%
1980 535
 
0.4%
Other values (30) 13648
9.7%
ValueCountFrequency (%)
1969 380
0.3%
1970 402
0.3%
1971 276
0.2%
1972 236
0.2%
1973 232
0.2%
1974 234
0.2%
1975 244
0.2%
1976 421
0.3%
1977 532
0.4%
1978 519
0.4%
ValueCountFrequency (%)
2008 31025
22.2%
2007 31305
22.4%
2006 31118
22.2%
2005 29656
21.2%
2004 505
 
0.4%
2003 459
 
0.3%
2002 491
 
0.4%
2001 491
 
0.4%
2000 520
 
0.4%
1999 508
 
0.4%

month
Real number (ℝ)

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.5510071
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2024-07-30T18:02:28.659144image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median7
Q39
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.4225862
Coefficient of variation (CV)0.52245191
Kurtosis-1.186765
Mean6.5510071
Median Absolute Deviation (MAD)3
Skewness-0.02758067
Sum917141
Variance11.714096
MonotonicityNot monotonic
2024-07-30T18:02:29.087222image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
8 12458
8.9%
7 12321
8.8%
9 11995
8.6%
6 11723
8.4%
5 11684
8.3%
3 11681
8.3%
10 11671
8.3%
12 11669
8.3%
1 11559
8.3%
11 11367
8.1%
Other values (2) 21872
15.6%
ValueCountFrequency (%)
1 11559
8.3%
2 10674
7.6%
3 11681
8.3%
4 11198
8.0%
5 11684
8.3%
6 11723
8.4%
7 12321
8.8%
8 12458
8.9%
9 11995
8.6%
10 11671
8.3%
ValueCountFrequency (%)
12 11669
8.3%
11 11367
8.1%
10 11671
8.3%
9 11995
8.6%
8 12458
8.9%
7 12321
8.8%
6 11723
8.4%
5 11684
8.3%
4 11198
8.0%
3 11681
8.3%

day
Real number (ℝ)

MISSING 

Distinct31
Distinct (%)0.4%
Missing131346
Missing (%)93.8%
Infinite0
Infinite (%)0.0%
Mean15.753062
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2024-07-30T18:02:29.561016image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median16
Q323
95-th percentile30
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.7700393
Coefficient of variation (CV)0.55671966
Kurtosis-1.1853201
Mean15.753062
Median Absolute Deviation (MAD)8
Skewness0.019430249
Sum136327
Variance76.91359
MonotonicityNot monotonic
2024-07-30T18:02:30.021000image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
8 321
 
0.2%
14 315
 
0.2%
23 305
 
0.2%
10 304
 
0.2%
20 301
 
0.2%
27 300
 
0.2%
4 298
 
0.2%
13 295
 
0.2%
17 294
 
0.2%
22 292
 
0.2%
Other values (21) 5629
 
4.0%
(Missing) 131346
93.8%
ValueCountFrequency (%)
1 249
0.2%
2 290
0.2%
3 283
0.2%
4 298
0.2%
5 286
0.2%
6 249
0.2%
7 284
0.2%
8 321
0.2%
9 274
0.2%
10 304
0.2%
ValueCountFrequency (%)
31 170
0.1%
30 278
0.2%
29 263
0.2%
28 275
0.2%
27 300
0.2%
26 282
0.2%
25 255
0.2%
24 256
0.2%
23 305
0.2%
22 292
0.2%

wday
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct7
Distinct (%)< 0.1%
Missing8654
Missing (%)6.2%
Infinite0
Infinite (%)0.0%
Mean4.0599257
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2024-07-30T18:02:30.367411image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median4
Q36
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.8304452
Coefficient of variation (CV)0.45085683
Kurtosis-1.1002734
Mean4.0599257
Median Absolute Deviation (MAD)2
Skewness-0.023394436
Sum533255
Variance3.3505297
MonotonicityNot monotonic
2024-07-30T18:02:30.689230image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
3 21699
15.5%
5 21469
15.3%
4 21427
15.3%
6 20957
15.0%
2 19719
14.1%
7 13975
10.0%
1 12100
8.6%
(Missing) 8654
 
6.2%
ValueCountFrequency (%)
1 12100
8.6%
2 19719
14.1%
3 21699
15.5%
4 21427
15.3%
5 21469
15.3%
6 20957
15.0%
7 13975
10.0%
ValueCountFrequency (%)
7 13975
10.0%
6 20957
15.0%
5 21469
15.3%
4 21427
15.3%
3 21699
15.5%
2 19719
14.1%
1 12100
8.6%

state
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing123104
Missing (%)87.9%
Memory size1.1 MiB
AL
14258 
AK
2367 
AR
 
271

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters33792
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAK
2nd rowAK
3rd rowAK
4th rowAK
5th rowAK

Common Values

ValueCountFrequency (%)
AL 14258
 
10.2%
AK 2367
 
1.7%
AR 271
 
0.2%
(Missing) 123104
87.9%

Length

2024-07-30T18:02:31.108253image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-30T18:02:31.451924image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
al 14258
84.4%
ak 2367
 
14.0%
ar 271
 
1.6%

Most occurring characters

ValueCountFrequency (%)
A 16896
50.0%
L 14258
42.2%
K 2367
 
7.0%
R 271
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 33792
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 16896
50.0%
L 14258
42.2%
K 2367
 
7.0%
R 271
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 33792
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 16896
50.0%
L 14258
42.2%
K 2367
 
7.0%
R 271
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 33792
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 16896
50.0%
L 14258
42.2%
K 2367
 
7.0%
R 271
 
0.8%

is_male
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size136.8 KiB
True
71546 
False
68454 
ValueCountFrequency (%)
True 71546
51.1%
False 68454
48.9%
2024-07-30T18:02:31.761877image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

child_race
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct8
Distinct (%)0.1%
Missing124068
Missing (%)88.6%
Infinite0
Infinite (%)0.0%
Mean3.6471253
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2024-07-30T18:02:32.057512image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q39
95-th percentile9
Maximum9
Range8
Interquartile range (IQR)8

Descriptive statistics

Standard deviation3.50963
Coefficient of variation (CV)0.96230037
Kurtosis-1.2381479
Mean3.6471253
Median Absolute Deviation (MAD)1
Skewness0.83154627
Sum58106
Variance12.317503
MonotonicityNot monotonic
2024-07-30T18:02:32.414024image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
1 7318
 
5.2%
9 4714
 
3.4%
2 3472
 
2.5%
3 380
 
0.3%
7 23
 
< 0.1%
4 13
 
< 0.1%
5 7
 
< 0.1%
6 5
 
< 0.1%
(Missing) 124068
88.6%
ValueCountFrequency (%)
1 7318
5.2%
2 3472
2.5%
3 380
 
0.3%
4 13
 
< 0.1%
5 7
 
< 0.1%
6 5
 
< 0.1%
7 23
 
< 0.1%
9 4714
3.4%
ValueCountFrequency (%)
9 4714
3.4%
7 23
 
< 0.1%
6 5
 
< 0.1%
5 7
 
< 0.1%
4 13
 
< 0.1%
3 380
 
0.3%
2 3472
2.5%
1 7318
5.2%

weight_pounds
Real number (ℝ)

Distinct3568
Distinct (%)2.6%
Missing138
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean7.2109486
Minimum0.50044933
Maximum16.459712
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2024-07-30T18:02:32.813138image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum0.50044933
5-th percentile5.0000841
Q16.5609569
median7.3127332
Q38.0248263
95-th percentile9.124933
Maximum16.459712
Range15.959263
Interquartile range (IQR)1.4638694

Descriptive statistics

Standard deviation1.3211065
Coefficient of variation (CV)0.18320842
Kurtosis2.8572719
Mean7.2109486
Median Absolute Deviation (MAD)0.74957169
Skewness-0.89931276
Sum1008537.7
Variance1.7453224
MonotonicityNot monotonic
2024-07-30T18:02:33.521503image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.374462664 2246
 
1.6%
7.561855587 2200
 
1.6%
6.999676819 2187
 
1.6%
7.187069741 2186
 
1.6%
7.500126153 2051
 
1.5%
7.749248509 1960
 
1.4%
7.251003797 1901
 
1.4%
7.312733231 1898
 
1.4%
7.687519076 1888
 
1.3%
7.125340308 1868
 
1.3%
Other values (3558) 119477
85.3%
ValueCountFrequency (%)
0.5004493347 8
< 0.1%
0.5070632026 2
 
< 0.1%
0.5092678252 1
 
< 0.1%
0.5158816931 1
 
< 0.1%
0.5180863157 1
 
< 0.1%
0.5291094288 1
 
< 0.1%
0.5401325419 1
 
< 0.1%
0.5445417871 1
 
< 0.1%
0.5599741455 1
 
< 0.1%
0.5621787681 6
< 0.1%
ValueCountFrequency (%)
16.45971248 1
< 0.1%
15.62636513 1
< 0.1%
14.36752561 1
< 0.1%
13.81196071 1
< 0.1%
13.75023128 1
< 0.1%
13.56945223 1
< 0.1%
13.31151138 1
< 0.1%
13.00065959 1
< 0.1%
12.86397299 1
< 0.1%
12.85956374 1
< 0.1%

plurality
Categorical

IMBALANCE 

Distinct5
Distinct (%)< 0.1%
Missing782
Missing (%)0.6%
Memory size1.1 MiB
1.0
134663 
2.0
 
4356
3.0
 
187
4.0
 
10
5.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters417654
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row2.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 134663
96.2%
2.0 4356
 
3.1%
3.0 187
 
0.1%
4.0 10
 
< 0.1%
5.0 2
 
< 0.1%
(Missing) 782
 
0.6%

Length

2024-07-30T18:02:33.894874image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-30T18:02:34.181092image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0 134663
96.7%
2.0 4356
 
3.1%
3.0 187
 
0.1%
4.0 10
 
< 0.1%
5.0 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
. 139218
33.3%
0 139218
33.3%
1 134663
32.2%
2 4356
 
1.0%
3 187
 
< 0.1%
4 10
 
< 0.1%
5 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 417654
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 139218
33.3%
0 139218
33.3%
1 134663
32.2%
2 4356
 
1.0%
3 187
 
< 0.1%
4 10
 
< 0.1%
5 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 417654
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 139218
33.3%
0 139218
33.3%
1 134663
32.2%
2 4356
 
1.0%
3 187
 
< 0.1%
4 10
 
< 0.1%
5 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 417654
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 139218
33.3%
0 139218
33.3%
1 134663
32.2%
2 4356
 
1.0%
3 187
 
< 0.1%
4 10
 
< 0.1%
5 2
 
< 0.1%

apgar_1min
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct11
Distinct (%)0.1%
Missing127032
Missing (%)90.7%
Infinite0
Infinite (%)0.0%
Mean37.750463
Minimum1
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2024-07-30T18:02:34.467646image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6
Q18
median9
Q399
95-th percentile99
Maximum99
Range98
Interquartile range (IQR)91

Descriptive statistics

Standard deviation42.63343
Coefficient of variation (CV)1.1293485
Kurtosis-1.4502484
Mean37.750463
Median Absolute Deviation (MAD)1
Skewness0.73906407
Sum489548
Variance1817.6094
MonotonicityNot monotonic
2024-07-30T18:02:34.765033image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
99 4230
 
3.0%
9 3850
 
2.8%
8 3082
 
2.2%
7 733
 
0.5%
6 291
 
0.2%
10 291
 
0.2%
5 153
 
0.1%
4 119
 
0.1%
3 80
 
0.1%
1 74
 
0.1%
(Missing) 127032
90.7%
ValueCountFrequency (%)
1 74
 
0.1%
2 65
 
< 0.1%
3 80
 
0.1%
4 119
 
0.1%
5 153
 
0.1%
6 291
 
0.2%
7 733
 
0.5%
8 3082
2.2%
9 3850
2.8%
10 291
 
0.2%
ValueCountFrequency (%)
99 4230
3.0%
10 291
 
0.2%
9 3850
2.8%
8 3082
2.2%
7 733
 
0.5%
6 291
 
0.2%
5 153
 
0.1%
4 119
 
0.1%
3 80
 
0.1%
2 65
 
< 0.1%

apgar_5min
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED 

Distinct12
Distinct (%)< 0.1%
Missing11933
Missing (%)8.5%
Infinite0
Infinite (%)0.0%
Mean8.9421162
Minimum0
Maximum99
Zeros60
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2024-07-30T18:02:35.065209image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile8
Q19
median9
Q39
95-th percentile10
Maximum99
Range99
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.8132854
Coefficient of variation (CV)0.31461069
Kurtosis940.30931
Mean8.9421162
Median Absolute Deviation (MAD)0
Skewness29.332237
Sum1145190
Variance7.9145746
MonotonicityNot monotonic
2024-07-30T18:02:35.371160image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
9 104817
74.9%
8 11397
 
8.1%
10 7626
 
5.4%
7 2068
 
1.5%
6 778
 
0.6%
5 376
 
0.3%
1 274
 
0.2%
4 221
 
0.2%
2 171
 
0.1%
3 164
 
0.1%
Other values (2) 175
 
0.1%
(Missing) 11933
 
8.5%
ValueCountFrequency (%)
0 60
 
< 0.1%
1 274
 
0.2%
2 171
 
0.1%
3 164
 
0.1%
4 221
 
0.2%
5 376
 
0.3%
6 778
 
0.6%
7 2068
 
1.5%
8 11397
 
8.1%
9 104817
74.9%
ValueCountFrequency (%)
99 115
 
0.1%
10 7626
 
5.4%
9 104817
74.9%
8 11397
 
8.1%
7 2068
 
1.5%
6 778
 
0.6%
5 376
 
0.3%
4 221
 
0.2%
3 164
 
0.1%
2 171
 
0.1%

mother_residence_state
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct23
Distinct (%)0.1%
Missing123104
Missing (%)87.9%
Memory size1.1 MiB
AL
13973 
AK
2362 
AR
 
263
FL
 
87
MS
 
64
Other values (18)
 
147

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters33792
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11 ?
Unique (%)0.1%

Sample

1st rowAK
2nd rowAK
3rd rowAK
4th rowAK
5th rowAK

Common Values

ValueCountFrequency (%)
AL 13973
 
10.0%
AK 2362
 
1.7%
AR 263
 
0.2%
FL 87
 
0.1%
MS 64
 
< 0.1%
GA 61
 
< 0.1%
TN 61
 
< 0.1%
OK 6
 
< 0.1%
NC 2
 
< 0.1%
TX 2
 
< 0.1%
Other values (13) 15
 
< 0.1%
(Missing) 123104
87.9%

Length

2024-07-30T18:02:35.696418image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
al 13973
82.7%
ak 2362
 
14.0%
ar 263
 
1.6%
fl 87
 
0.5%
ms 64
 
0.4%
ga 61
 
0.4%
tn 61
 
0.4%
ok 6
 
< 0.1%
nc 2
 
< 0.1%
tx 2
 
< 0.1%
Other values (13) 15
 
0.1%

Most occurring characters

ValueCountFrequency (%)
A 16662
49.3%
L 14061
41.6%
K 2369
 
7.0%
R 263
 
0.8%
F 87
 
0.3%
M 69
 
0.2%
S 67
 
0.2%
N 66
 
0.2%
T 63
 
0.2%
G 61
 
0.2%
Other values (8) 24
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 33792
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 16662
49.3%
L 14061
41.6%
K 2369
 
7.0%
R 263
 
0.8%
F 87
 
0.3%
M 69
 
0.2%
S 67
 
0.2%
N 66
 
0.2%
T 63
 
0.2%
G 61
 
0.2%
Other values (8) 24
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 33792
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 16662
49.3%
L 14061
41.6%
K 2369
 
7.0%
R 263
 
0.8%
F 87
 
0.3%
M 69
 
0.2%
S 67
 
0.2%
N 66
 
0.2%
T 63
 
0.2%
G 61
 
0.2%
Other values (8) 24
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 33792
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 16662
49.3%
L 14061
41.6%
K 2369
 
7.0%
R 263
 
0.8%
F 87
 
0.3%
M 69
 
0.2%
S 67
 
0.2%
N 66
 
0.2%
T 63
 
0.2%
G 61
 
0.2%
Other values (8) 24
 
0.1%

mother_race
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct15
Distinct (%)< 0.1%
Missing64325
Missing (%)45.9%
Infinite0
Infinite (%)0.0%
Mean2.8684638
Minimum1
Maximum78
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2024-07-30T18:02:35.985589image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile3
Maximum78
Range77
Interquartile range (IQR)1

Descriptive statistics

Standard deviation9.9756692
Coefficient of variation (CV)3.4777044
Kurtosis45.862648
Mean2.8684638
Median Absolute Deviation (MAD)0
Skewness6.7918093
Sum217071
Variance99.513977
MonotonicityNot monotonic
2024-07-30T18:02:36.324183image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
1 55327
39.5%
2 15424
 
11.0%
3 1452
 
1.0%
78 912
 
0.7%
4 555
 
0.4%
18 554
 
0.4%
7 474
 
0.3%
68 376
 
0.3%
48 152
 
0.1%
28 150
 
0.1%
Other values (5) 299
 
0.2%
(Missing) 64325
45.9%
ValueCountFrequency (%)
1 55327
39.5%
2 15424
 
11.0%
3 1452
 
1.0%
4 555
 
0.4%
5 141
 
0.1%
6 34
 
< 0.1%
7 474
 
0.3%
9 99
 
0.1%
18 554
 
0.4%
28 150
 
0.1%
ValueCountFrequency (%)
78 912
0.7%
68 376
0.3%
58 4
 
< 0.1%
48 152
 
0.1%
38 21
 
< 0.1%
28 150
 
0.1%
18 554
0.4%
9 99
 
0.1%
7 474
0.3%
6 34
 
< 0.1%

mother_age
Real number (ℝ)

Distinct39
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.124679
Minimum12
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2024-07-30T18:02:36.660835image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum12
5-th percentile18
Q122
median27
Q332
95-th percentile38
Maximum50
Range38
Interquartile range (IQR)10

Descriptive statistics

Standard deviation6.1616839
Coefficient of variation (CV)0.22716154
Kurtosis-0.56389428
Mean27.124679
Median Absolute Deviation (MAD)5
Skewness0.27388388
Sum3797455
Variance37.966348
MonotonicityNot monotonic
2024-07-30T18:02:37.106506image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
26 8013
 
5.7%
27 7856
 
5.6%
28 7779
 
5.6%
24 7756
 
5.5%
25 7707
 
5.5%
23 7566
 
5.4%
29 7485
 
5.3%
22 7309
 
5.2%
30 7262
 
5.2%
21 7041
 
5.0%
Other values (29) 64226
45.9%
ValueCountFrequency (%)
12 7
 
< 0.1%
13 46
 
< 0.1%
14 240
 
0.2%
15 704
 
0.5%
16 1593
 
1.1%
17 2822
2.0%
18 4373
3.1%
19 5895
4.2%
20 6738
4.8%
21 7041
5.0%
ValueCountFrequency (%)
50 14
 
< 0.1%
49 8
 
< 0.1%
48 6
 
< 0.1%
47 32
 
< 0.1%
46 50
 
< 0.1%
45 90
 
0.1%
44 177
 
0.1%
43 373
0.3%
42 586
0.4%
41 855
0.6%

gestation_weeks
Real number (ℝ)

MISSING 

Distinct37
Distinct (%)< 0.1%
Missing2301
Missing (%)1.6%
Infinite0
Infinite (%)0.0%
Mean38.90883
Minimum17
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2024-07-30T18:02:37.589339image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum17
5-th percentile34
Q138
median39
Q340
95-th percentile42
Maximum99
Range82
Interquartile range (IQR)2

Descriptive statistics

Standard deviation5.0584691
Coefficient of variation (CV)0.13000825
Kurtosis101.2077
Mean38.90883
Median Absolute Deviation (MAD)1
Skewness8.3871079
Sum5357707
Variance25.588109
MonotonicityNot monotonic
2024-07-30T18:02:38.014551image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
39 34983
25.0%
40 26196
18.7%
38 25540
18.2%
37 12368
 
8.8%
41 12140
 
8.7%
36 6249
 
4.5%
42 4280
 
3.1%
35 3627
 
2.6%
34 2229
 
1.6%
43 2162
 
1.5%
Other values (27) 7925
 
5.7%
(Missing) 2301
 
1.6%
ValueCountFrequency (%)
17 16
 
< 0.1%
18 20
 
< 0.1%
19 33
 
< 0.1%
20 42
 
< 0.1%
21 64
 
< 0.1%
22 77
0.1%
23 120
0.1%
24 140
0.1%
25 147
0.1%
26 192
0.1%
ValueCountFrequency (%)
99 714
0.5%
52 5
 
< 0.1%
51 5
 
< 0.1%
50 13
 
< 0.1%
49 12
 
< 0.1%
48 10
 
< 0.1%
47 197
 
0.1%
46 302
 
0.2%
45 564
0.4%
44 1111
0.8%

lmp
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size1.1 MiB

mother_married
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing12
Missing (%)< 0.1%
Memory size1.1 MiB
True
87811 
False
52177 
(Missing)
 
12
ValueCountFrequency (%)
True 87811
62.7%
False 52177
37.3%
(Missing) 12
 
< 0.1%
2024-07-30T18:02:38.394547image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

mother_birth_state
Text

MISSING 

Distinct61
Distinct (%)0.4%
Missing123747
Missing (%)88.4%
Memory size1.1 MiB
2024-07-30T18:02:38.687948image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Length

Max length7
Median length2
Mean length2.14182
Min length2

Characters and Unicode

Total characters34811
Distinct characters36
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowAK
2nd rowIL
3rd rowMO
4th rowPA
5th rowNE
ValueCountFrequency (%)
al 9783
60.2%
ak 740
 
4.6%
ga 429
 
2.6%
fl 423
 
2.6%
foreign 383
 
2.4%
ca 349
 
2.1%
ms 317
 
2.0%
tn 280
 
1.7%
tx 267
 
1.6%
il 261
 
1.6%
Other values (51) 3021
 
18.6%
2024-07-30T18:02:39.364474image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 12104
34.8%
L 10612
30.5%
K 964
 
2.8%
N 940
 
2.7%
M 926
 
2.7%
F 806
 
2.3%
I 769
 
2.2%
C 679
 
2.0%
T 666
 
1.9%
O 517
 
1.5%
Other values (26) 5828
16.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 34811
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 12104
34.8%
L 10612
30.5%
K 964
 
2.8%
N 940
 
2.7%
M 926
 
2.7%
F 806
 
2.3%
I 769
 
2.2%
C 679
 
2.0%
T 666
 
1.9%
O 517
 
1.5%
Other values (26) 5828
16.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 34811
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 12104
34.8%
L 10612
30.5%
K 964
 
2.8%
N 940
 
2.7%
M 926
 
2.7%
F 806
 
2.3%
I 769
 
2.2%
C 679
 
2.0%
T 666
 
1.9%
O 517
 
1.5%
Other values (26) 5828
16.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 34811
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 12104
34.8%
L 10612
30.5%
K 964
 
2.8%
N 940
 
2.7%
M 926
 
2.7%
F 806
 
2.3%
I 769
 
2.2%
C 679
 
2.0%
T 666
 
1.9%
O 517
 
1.5%
Other values (26) 5828
16.7%

cigarette_use
Boolean

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing81576
Missing (%)58.3%
Memory size1.1 MiB
False
52656 
True
 
5768
(Missing)
81576 
ValueCountFrequency (%)
False 52656
37.6%
True 5768
 
4.1%
(Missing) 81576
58.3%
2024-07-30T18:02:39.685492image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

cigarettes_per_day
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct32
Distinct (%)0.6%
Missing134232
Missing (%)95.9%
Infinite0
Infinite (%)0.0%
Mean15.087205
Minimum1
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2024-07-30T18:02:40.029584image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q15
median10
Q310.25
95-th percentile99
Maximum99
Range98
Interquartile range (IQR)5.25

Descriptive statistics

Standard deviation23.312643
Coefficient of variation (CV)1.5451929
Kurtosis8.3398944
Mean15.087205
Median Absolute Deviation (MAD)5
Skewness3.0950497
Sum87023
Variance543.4793
MonotonicityNot monotonic
2024-07-30T18:02:40.405761image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
10 1746
 
1.2%
5 707
 
0.5%
20 671
 
0.5%
3 394
 
0.3%
99 388
 
0.3%
2 361
 
0.3%
4 312
 
0.2%
6 246
 
0.2%
1 231
 
0.2%
15 176
 
0.1%
Other values (22) 536
 
0.4%
(Missing) 134232
95.9%
ValueCountFrequency (%)
1 231
 
0.2%
2 361
 
0.3%
3 394
 
0.3%
4 312
 
0.2%
5 707
0.5%
6 246
 
0.2%
7 170
 
0.1%
8 130
 
0.1%
9 29
 
< 0.1%
10 1746
1.2%
ValueCountFrequency (%)
99 388
0.3%
60 2
 
< 0.1%
46 1
 
< 0.1%
40 15
 
< 0.1%
36 1
 
< 0.1%
35 1
 
< 0.1%
30 36
 
< 0.1%
27 2
 
< 0.1%
25 8
 
< 0.1%
24 2
 
< 0.1%

alcohol_use
Boolean

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing74321
Missing (%)53.1%
Memory size1.1 MiB
False
59782 
True
 
5897
(Missing)
74321 
ValueCountFrequency (%)
False 59782
42.7%
True 5897
 
4.2%
(Missing) 74321
53.1%
2024-07-30T18:02:40.747855image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

drinks_per_week
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct14
Distinct (%)0.4%
Missing136403
Missing (%)97.4%
Infinite0
Infinite (%)0.0%
Mean3.2749513
Minimum0
Maximum99
Zeros3335
Zeros (%)2.4%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2024-07-30T18:02:41.092306image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum99
Range99
Interquartile range (IQR)0

Descriptive statistics

Standard deviation17.424963
Coefficient of variation (CV)5.3206784
Kurtosis26.180972
Mean3.2749513
Median Absolute Deviation (MAD)0
Skewness5.3019109
Sum11780
Variance303.62933
MonotonicityNot monotonic
2024-07-30T18:02:41.518399image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0 3335
 
2.4%
99 115
 
0.1%
1 72
 
0.1%
2 36
 
< 0.1%
6 11
 
< 0.1%
3 8
 
< 0.1%
5 6
 
< 0.1%
4 5
 
< 0.1%
7 4
 
< 0.1%
8 1
 
< 0.1%
Other values (4) 4
 
< 0.1%
(Missing) 136403
97.4%
ValueCountFrequency (%)
0 3335
2.4%
1 72
 
0.1%
2 36
 
< 0.1%
3 8
 
< 0.1%
4 5
 
< 0.1%
5 6
 
< 0.1%
6 11
 
< 0.1%
7 4
 
< 0.1%
8 1
 
< 0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
99 115
0.1%
42 1
 
< 0.1%
14 1
 
< 0.1%
10 1
 
< 0.1%
9 1
 
< 0.1%
8 1
 
< 0.1%
7 4
 
< 0.1%
6 11
 
< 0.1%
5 6
 
< 0.1%
4 5
 
< 0.1%

weight_gain_pounds
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct99
Distinct (%)0.1%
Missing11178
Missing (%)8.0%
Infinite0
Infinite (%)0.0%
Mean39.383785
Minimum1
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2024-07-30T18:02:41.983123image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile11
Q123
median32
Q345
95-th percentile99
Maximum99
Range98
Interquartile range (IQR)22

Descriptive statistics

Standard deviation25.401934
Coefficient of variation (CV)0.64498457
Kurtosis0.96154864
Mean39.383785
Median Absolute Deviation (MAD)10
Skewness1.352962
Sum5073498
Variance645.25824
MonotonicityNot monotonic
2024-07-30T18:02:42.438778image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
99 15247
 
10.9%
30 8910
 
6.4%
20 6180
 
4.4%
25 5965
 
4.3%
40 5867
 
4.2%
35 5242
 
3.7%
32 3002
 
2.1%
50 2866
 
2.0%
28 2859
 
2.0%
26 2686
 
1.9%
Other values (89) 69998
50.0%
(Missing) 11178
 
8.0%
ValueCountFrequency (%)
1 249
 
0.2%
2 330
 
0.2%
3 330
 
0.2%
4 387
 
0.3%
5 614
 
0.4%
6 488
 
0.3%
7 627
 
0.4%
8 678
 
0.5%
9 586
 
0.4%
10 1898
1.4%
ValueCountFrequency (%)
99 15247
10.9%
98 135
 
0.1%
97 5
 
< 0.1%
96 4
 
< 0.1%
95 19
 
< 0.1%
94 10
 
< 0.1%
93 13
 
< 0.1%
92 6
 
< 0.1%
91 7
 
< 0.1%
90 51
 
< 0.1%

born_alive_alive
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct18
Distinct (%)< 0.1%
Missing93590
Missing (%)66.8%
Infinite0
Infinite (%)0.0%
Mean1.054471
Minimum0
Maximum77
Zeros18875
Zeros (%)13.5%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2024-07-30T18:02:42.901440image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile3
Maximum77
Range77
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.5214944
Coefficient of variation (CV)1.4428983
Kurtosis743.95084
Mean1.054471
Median Absolute Deviation (MAD)1
Skewness16.641932
Sum48938
Variance2.3149453
MonotonicityNot monotonic
2024-07-30T18:02:43.341515image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
0 18875
 
13.5%
1 15202
 
10.9%
2 7482
 
5.3%
3 2912
 
2.1%
4 1058
 
0.8%
5 438
 
0.3%
6 215
 
0.2%
7 110
 
0.1%
8 41
 
< 0.1%
9 34
 
< 0.1%
Other values (8) 43
 
< 0.1%
(Missing) 93590
66.8%
ValueCountFrequency (%)
0 18875
13.5%
1 15202
10.9%
2 7482
 
5.3%
3 2912
 
2.1%
4 1058
 
0.8%
5 438
 
0.3%
6 215
 
0.2%
7 110
 
0.1%
8 41
 
< 0.1%
9 34
 
< 0.1%
ValueCountFrequency (%)
77 5
 
< 0.1%
55 2
 
< 0.1%
35 1
 
< 0.1%
14 1
 
< 0.1%
13 5
 
< 0.1%
12 6
 
< 0.1%
11 9
 
< 0.1%
10 14
 
< 0.1%
9 34
< 0.1%
8 41
< 0.1%

born_alive_dead
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct12
Distinct (%)< 0.1%
Missing93623
Missing (%)66.9%
Infinite0
Infinite (%)0.0%
Mean0.060072881
Minimum0
Maximum77
Zeros45519
Zeros (%)32.5%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2024-07-30T18:02:43.709764image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum77
Range77
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.6336883
Coefficient of variation (CV)27.195104
Kurtosis1891.9174
Mean0.060072881
Median Absolute Deviation (MAD)0
Skewness42.83459
Sum2786
Variance2.6689373
MonotonicityNot monotonic
2024-07-30T18:02:44.126886image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 45519
32.5%
1 707
 
0.5%
2 91
 
0.1%
3 21
 
< 0.1%
77 15
 
< 0.1%
55 11
 
< 0.1%
4 5
 
< 0.1%
5 3
 
< 0.1%
6 2
 
< 0.1%
10 1
 
< 0.1%
Other values (2) 2
 
< 0.1%
(Missing) 93623
66.9%
ValueCountFrequency (%)
0 45519
32.5%
1 707
 
0.5%
2 91
 
0.1%
3 21
 
< 0.1%
4 5
 
< 0.1%
5 3
 
< 0.1%
6 2
 
< 0.1%
8 1
 
< 0.1%
9 1
 
< 0.1%
10 1
 
< 0.1%
ValueCountFrequency (%)
77 15
 
< 0.1%
55 11
 
< 0.1%
10 1
 
< 0.1%
9 1
 
< 0.1%
8 1
 
< 0.1%
6 2
 
< 0.1%
5 3
 
< 0.1%
4 5
 
< 0.1%
3 21
 
< 0.1%
2 91
0.1%

born_dead
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct16
Distinct (%)< 0.1%
Missing93652
Missing (%)66.9%
Infinite0
Infinite (%)0.0%
Mean0.36066281
Minimum0
Maximum77
Zeros36177
Zeros (%)25.8%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2024-07-30T18:02:44.466598image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum77
Range77
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.7798383
Coefficient of variation (CV)4.9349095
Kurtosis1321.5141
Mean0.36066281
Median Absolute Deviation (MAD)0
Skewness32.972342
Sum16716
Variance3.1678245
MonotonicityNot monotonic
2024-07-30T18:02:44.851032image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0 36177
 
25.8%
1 7052
 
5.0%
2 2029
 
1.4%
3 693
 
0.5%
4 227
 
0.2%
5 82
 
0.1%
6 30
 
< 0.1%
7 21
 
< 0.1%
77 15
 
< 0.1%
55 11
 
< 0.1%
Other values (6) 11
 
< 0.1%
(Missing) 93652
66.9%
ValueCountFrequency (%)
0 36177
25.8%
1 7052
 
5.0%
2 2029
 
1.4%
3 693
 
0.5%
4 227
 
0.2%
5 82
 
0.1%
6 30
 
< 0.1%
7 21
 
< 0.1%
8 3
 
< 0.1%
10 2
 
< 0.1%
ValueCountFrequency (%)
77 15
< 0.1%
55 11
 
< 0.1%
18 1
 
< 0.1%
14 1
 
< 0.1%
12 2
 
< 0.1%
11 2
 
< 0.1%
10 2
 
< 0.1%
8 3
 
< 0.1%
7 21
< 0.1%
6 30
< 0.1%

ever_born
Real number (ℝ)

HIGH CORRELATION 

Distinct15
Distinct (%)< 0.1%
Missing723
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean2.0813989
Minimum1
Maximum15
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2024-07-30T18:02:45.280349image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q33
95-th percentile4
Maximum15
Range14
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.2556892
Coefficient of variation (CV)0.60329098
Kurtosis5.3525575
Mean2.0813989
Median Absolute Deviation (MAD)1
Skewness1.7741727
Sum289891
Variance1.5767554
MonotonicityNot monotonic
2024-07-30T18:02:45.634408image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
1 55565
39.7%
2 44461
31.8%
3 23254
16.6%
4 9588
 
6.8%
5 3613
 
2.6%
6 1446
 
1.0%
7 659
 
0.5%
8 530
 
0.4%
9 54
 
< 0.1%
10 44
 
< 0.1%
Other values (5) 63
 
< 0.1%
(Missing) 723
 
0.5%
ValueCountFrequency (%)
1 55565
39.7%
2 44461
31.8%
3 23254
16.6%
4 9588
 
6.8%
5 3613
 
2.6%
6 1446
 
1.0%
7 659
 
0.5%
8 530
 
0.4%
9 54
 
< 0.1%
10 44
 
< 0.1%
ValueCountFrequency (%)
15 4
 
< 0.1%
14 8
 
< 0.1%
13 12
 
< 0.1%
12 13
 
< 0.1%
11 26
 
< 0.1%
10 44
 
< 0.1%
9 54
 
< 0.1%
8 530
 
0.4%
7 659
0.5%
6 1446
1.0%

father_race
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct16
Distinct (%)< 0.1%
Missing64325
Missing (%)45.9%
Infinite0
Infinite (%)0.0%
Mean15.978077
Minimum1
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2024-07-30T18:02:45.962837image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile99
Maximum99
Range98
Interquartile range (IQR)1

Descriptive statistics

Standard deviation33.844786
Coefficient of variation (CV)2.1182014
Kurtosis1.9857813
Mean15.978077
Median Absolute Deviation (MAD)0
Skewness1.9781829
Sum1209141
Variance1145.4695
MonotonicityNot monotonic
2024-07-30T18:02:46.356797image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
1 48471
34.6%
99 10056
 
7.2%
2 9783
 
7.0%
9 3677
 
2.6%
3 887
 
0.6%
78 789
 
0.6%
18 526
 
0.4%
4 454
 
0.3%
68 354
 
0.3%
7 310
 
0.2%
Other values (6) 368
 
0.3%
(Missing) 64325
45.9%
ValueCountFrequency (%)
1 48471
34.6%
2 9783
 
7.0%
3 887
 
0.6%
4 454
 
0.3%
5 84
 
0.1%
6 17
 
< 0.1%
7 310
 
0.2%
9 3677
 
2.6%
18 526
 
0.4%
28 123
 
0.1%
ValueCountFrequency (%)
99 10056
7.2%
78 789
 
0.6%
68 354
 
0.3%
58 4
 
< 0.1%
48 122
 
0.1%
38 18
 
< 0.1%
28 123
 
0.1%
18 526
 
0.4%
9 3677
 
2.6%
7 310
 
0.2%

father_age
Real number (ℝ)

HIGH CORRELATION 

Distinct66
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.70925
Minimum11
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2024-07-30T18:02:47.009133image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile20
Q126
median31
Q339
95-th percentile99
Maximum99
Range88
Interquartile range (IQR)13

Descriptive statistics

Standard deviation25.403633
Coefficient of variation (CV)0.62402605
Kurtosis1.2746793
Mean40.70925
Median Absolute Deviation (MAD)6
Skewness1.6991856
Sum5699295
Variance645.34455
MonotonicityNot monotonic
2024-07-30T18:02:47.409920image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
99 21145
 
15.1%
29 6782
 
4.8%
28 6667
 
4.8%
30 6661
 
4.8%
31 6591
 
4.7%
27 6427
 
4.6%
26 6138
 
4.4%
32 6122
 
4.4%
33 5820
 
4.2%
25 5818
 
4.2%
Other values (56) 61829
44.2%
ValueCountFrequency (%)
11 1
 
< 0.1%
13 4
 
< 0.1%
14 19
 
< 0.1%
15 72
 
0.1%
16 240
 
0.2%
17 591
 
0.4%
18 1312
 
0.9%
19 2128
1.5%
20 2951
2.1%
21 3652
2.6%
ValueCountFrequency (%)
99 21145
15.1%
79 1
 
< 0.1%
76 1
 
< 0.1%
75 1
 
< 0.1%
73 1
 
< 0.1%
72 1
 
< 0.1%
71 2
 
< 0.1%
70 2
 
< 0.1%
69 2
 
< 0.1%
68 2
 
< 0.1%

record_weight
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
1
137979 
2
 
2021

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters140000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 137979
98.6%
2 2021
 
1.4%

Length

2024-07-30T18:02:47.867962image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-30T18:02:48.241187image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
1 137979
98.6%
2 2021
 
1.4%

Most occurring characters

ValueCountFrequency (%)
1 137979
98.6%
2 2021
 
1.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 140000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 137979
98.6%
2 2021
 
1.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 140000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 137979
98.6%
2 2021
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 140000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 137979
98.6%
2 2021
 
1.4%

Interactions

2024-07-30T18:02:16.957581image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:00:14.614311image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:00:20.671146image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:00:27.179178image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:00:33.475063image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:00:38.834892image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:00:43.867896image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:00:49.634705image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:00:56.139587image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:01:02.914531image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:01:09.176244image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:01:15.939209image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:01:21.681112image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:01:27.916776image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:01:33.648645image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:01:39.397236image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:01:45.937095image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:01:52.241897image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:01:58.544316image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:02:04.960494image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:02:11.070250image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:02:17.269648image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:00:14.885097image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:00:21.014016image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:00:27.496225image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:00:33.763518image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:00:39.090321image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:00:44.111604image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:00:49.953313image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:00:56.382139image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:01:03.234127image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:01:09.507474image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:01:16.232402image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:01:21.935141image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:01:28.207507image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:01:33.914789image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:01:39.732023image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:01:46.298385image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:01:52.566671image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:01:58.837703image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:02:05.289173image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:02:11.386612image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:02:17.550133image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:00:15.186488image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:00:21.289494image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:00:27.806660image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:00:34.038627image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:00:39.347473image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:00:44.585605image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:00:50.264766image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:00:56.625023image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:01:03.569827image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:01:09.868237image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:01:16.512878image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:01:22.195786image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:01:28.487921image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:01:34.164861image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:01:40.068202image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:01:46.574830image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:01:52.840871image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
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2024-07-30T18:01:06.599308image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:01:13.175268image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:01:19.144160image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:01:24.861522image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:01:31.046727image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:01:36.853608image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:01:43.384457image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:01:49.541512image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:01:55.968920image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:02:02.232561image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:02:08.560253image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:02:14.522339image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:02:20.660443image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:00:18.151337image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:00:24.658641image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:00:30.944046image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:00:36.989596image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:00:41.964344image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:00:47.301029image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:00:53.679802image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:00:59.696327image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:01:06.863286image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:01:13.452280image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:01:19.412345image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:01:25.147216image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:01:31.351121image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:01:37.121600image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:01:43.643827image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:01:49.833932image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:01:56.236694image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:02:02.580431image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:02:08.811009image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:02:14.765221image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:02:20.894926image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:00:18.430770image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:00:24.934576image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:00:31.223307image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:00:37.188261image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:00:42.205230image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:00:47.529709image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:00:53.964278image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:00:59.945883image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:01:07.139109image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:01:13.739878image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:01:19.680345image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:01:25.740679image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:01:31.663850image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:01:37.389630image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:01:43.911821image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:01:50.074664image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:01:56.500983image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:02:02.922206image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:02:09.089610image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:02:15.010788image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:02:21.121214image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:00:18.738103image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:00:25.220748image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:00:31.470766image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:00:37.384921image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:00:42.439601image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:00:47.790545image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:00:54.264684image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:01:00.354453image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:01:07.401235image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:01:14.001388image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:01:19.943407image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:01:26.026722image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:01:31.940352image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:01:37.639731image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:01:44.162442image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:01:50.399527image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:01:56.748315image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:02:03.216193image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:02:09.358823image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:02:15.252918image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:02:21.366028image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:00:19.064314image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:00:25.537127image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:00:31.768812image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:00:37.630389image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:00:42.677138image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:00:48.083238image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:00:54.552891image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:01:00.652511image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:01:07.711140image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:01:14.292032image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:01:20.297691image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:01:26.305313image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:01:32.252326image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:01:37.919170image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:01:44.450920image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:01:50.700017image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:01:57.047572image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:02:03.537442image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:02:09.623996image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:02:15.514601image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:02:21.610653image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:00:19.587028image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:00:25.891483image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:00:32.091755image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:00:37.875999image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:00:42.909940image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:00:48.362020image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:00:54.872431image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:01:00.996306image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:01:08.009914image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:01:14.603219image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:01:20.602752image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:01:26.607546image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:01:32.521806image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:01:38.219027image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:01:44.715056image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:01:51.018384image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:01:57.396847image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:02:03.824179image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:02:09.906407image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:02:15.778137image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:02:22.104864image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:00:19.862425image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:00:26.256798image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:00:32.382706image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:00:38.118978image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:00:43.159933image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:00:48.661630image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:00:55.241575image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:01:01.440375image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:01:08.327834image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:01:14.942046image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:01:20.893357image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:01:26.940049image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:01:32.808369image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:01:38.505682image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:01:44.995717image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:01:51.314897image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:01:57.703642image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:02:04.122105image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:02:10.193798image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:02:16.068980image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:02:22.344308image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:00:20.123848image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:00:26.609013image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:00:32.891174image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:00:38.354959image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:00:43.400461image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:00:48.975522image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:00:55.592152image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:01:02.059515image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:01:08.626590image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:01:15.337851image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:01:21.180081image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:01:27.221657image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:01:33.102655image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:01:38.775332image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:01:45.316431image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:01:51.610341image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:01:58.010530image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:02:04.405820image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:02:10.489758image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:02:16.408489image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:02:22.581924image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:00:20.386039image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:00:26.900698image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:00:33.190981image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:00:38.597394image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:00:43.641451image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:00:49.306653image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:00:55.882731image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:01:02.558552image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:01:08.897611image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:01:15.642009image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:01:21.436110image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:01:27.603362image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:01:33.385025image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:01:39.105068image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:01:45.628816image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:01:51.914676image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:01:58.281898image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:02:04.688032image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:02:10.783057image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2024-07-30T18:02:16.698038image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Correlations

2024-07-30T18:02:48.532263image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
alcohol_useapgar_1minapgar_5minborn_alive_aliveborn_alive_deadborn_deadchild_racecigarette_usecigarettes_per_daydaydrinks_per_weekever_bornfather_agefather_racegestation_weeksis_malemonthmother_agemother_marriedmother_racemother_residence_statepluralityrecord_weightsource_yearstatewdayweight_gain_poundsweight_poundsyear
alcohol_use1.0000.0690.0160.0000.0000.0120.1461.0001.0000.0001.0000.0690.1450.1470.0160.0010.0070.1110.1800.0460.0860.0101.0000.0830.0860.0040.0590.1000.083
apgar_1min0.0691.0000.2260.027-0.013-0.0000.6550.000NaN-0.008-0.035-0.023-0.014-0.026-0.1110.000-0.0030.0520.070-0.0310.0220.0171.0000.6370.000-0.0080.0110.0070.637
apgar_5min0.0160.2261.0000.034-0.006-0.025-0.0930.0300.026-0.012-0.0400.038-0.015-0.0290.1050.000-0.004-0.0080.032-0.0200.1060.0171.000-0.0880.131-0.004-0.0080.100-0.088
born_alive_alive0.0000.0270.0341.0000.0900.1500.0450.0000.1430.0120.0260.9780.179-0.003-0.0820.0000.0060.3730.0090.0410.0000.0000.0560.0190.018-0.004-0.0950.0520.019
born_alive_dead0.000-0.013-0.0060.0901.0000.0590.0000.0000.0330.0040.0550.1740.0290.025-0.0170.005-0.0010.0460.0110.0370.0490.0000.085-0.0510.0590.000-0.023-0.020-0.051
born_dead0.012-0.000-0.0250.1500.0591.0000.0490.0160.044-0.0090.0740.1980.079-0.001-0.0430.005-0.0050.1840.0100.0060.0510.0000.0850.0690.0600.009-0.0330.0020.069
child_race0.1460.655-0.0930.0450.0000.0491.0000.000NaN0.008-0.229-0.0010.2150.374-0.2000.0000.0030.0220.4280.4330.1700.0000.2450.6750.302-0.0100.001-0.0940.675
cigarette_use1.0000.0000.0300.0000.0000.0160.0001.0001.0000.0001.0000.0690.1670.1380.0220.0030.0000.1290.1880.0520.0000.0121.0000.0060.0450.0060.0620.1060.006
cigarettes_per_day1.000NaN0.0260.1430.0330.044NaN1.0001.000NaN0.0220.099-0.025-0.092-0.0190.000-0.0170.0770.056-0.1830.2590.0001.000-0.1020.2590.004-0.029-0.040-0.102
day0.000-0.008-0.0120.0120.004-0.0090.0080.000NaN1.000NaN0.004-0.0020.001-0.0150.0000.015-0.0150.0000.0110.0000.0110.0100.0040.000NaNNaN-0.0020.004
drinks_per_week1.000-0.035-0.0400.0260.0550.074-0.2291.0000.022NaN1.0000.0130.0790.040-0.0270.0040.0030.0920.0000.1590.0840.0001.000-0.2690.1240.027-0.0040.023-0.269
ever_born0.069-0.0230.0380.9780.1740.198-0.0010.0690.0990.0040.0131.0000.189-0.006-0.0910.002-0.0000.3550.0560.0240.0580.0370.070-0.0060.069-0.004-0.1040.041-0.006
father_age0.145-0.014-0.0150.1790.0290.0790.2150.167-0.025-0.0020.0790.1891.0000.456-0.0560.0060.0070.3920.5800.1790.0500.0270.0490.0090.1030.004-0.041-0.0310.009
father_race0.147-0.026-0.029-0.0030.025-0.0010.3740.138-0.0920.0010.040-0.0060.4561.000-0.0520.0000.008-0.2170.4570.6310.0130.0000.0710.0290.000-0.000-0.049-0.1570.029
gestation_weeks0.016-0.1110.105-0.082-0.017-0.043-0.2000.022-0.019-0.015-0.027-0.091-0.056-0.0521.0000.011-0.001-0.0630.056-0.0600.0180.1090.101-0.0560.033-0.0020.0550.379-0.056
is_male0.0010.0000.0000.0000.0050.0050.0000.0030.0000.0000.0040.0020.0060.0000.0111.0000.0000.0080.0040.0070.0000.0000.0000.0010.0070.0050.0250.1100.001
month0.007-0.003-0.0040.006-0.001-0.0050.0030.000-0.0170.0150.003-0.0000.0070.008-0.0010.0001.0000.0030.0150.0090.0170.0000.003-0.0040.021-0.000-0.019-0.007-0.004
mother_age0.1110.052-0.0080.3730.0460.1840.0220.1290.077-0.0150.0920.3550.392-0.217-0.0630.0080.0031.0000.438-0.0910.0340.0430.0670.0720.0710.004-0.0250.0870.072
mother_married0.1800.0700.0320.0090.0110.0100.4280.1880.0560.0000.0000.0560.5800.4570.0560.0040.0150.4381.0000.0770.0640.0300.0550.0930.0530.0260.0810.1180.093
mother_race0.046-0.031-0.0200.0410.0370.0060.4330.052-0.1830.0110.1590.0240.1790.631-0.0600.0070.009-0.0910.0771.0000.1070.0000.029-0.0350.053-0.000-0.047-0.146-0.035
mother_residence_state0.0860.0220.1060.0000.0490.0510.1700.0000.2590.0000.0840.0580.0500.0130.0180.0000.0170.0340.0640.1071.0000.0000.3500.1590.9970.0250.0090.0400.159
plurality0.0100.0170.0170.0000.0000.0000.0000.0120.0000.0110.0000.0370.0270.0000.1090.0000.0000.0430.0300.0000.0001.0000.0060.0120.0000.0030.0440.1830.012
record_weight1.0001.0001.0000.0560.0850.0850.2451.0001.0000.0101.0000.0700.0490.0710.1010.0000.0030.0670.0550.0290.3500.0061.0000.9330.3481.0001.0000.0200.933
source_year0.0830.637-0.0880.019-0.0510.0690.6750.006-0.1020.004-0.269-0.0060.0090.029-0.0560.001-0.0040.0720.093-0.0350.1590.0120.9331.0000.316-0.001-0.071-0.0181.000
state0.0860.0000.1310.0180.0590.0600.3020.0450.2590.0000.1240.0690.1030.0000.0330.0070.0210.0710.0530.0530.9970.0000.3480.3161.0000.0440.0440.0830.316
wday0.004-0.008-0.004-0.0040.0000.009-0.0100.0060.004NaN0.027-0.0040.004-0.000-0.0020.005-0.0000.0040.026-0.0000.0250.0031.000-0.0010.0441.0000.011-0.008-0.001
weight_gain_pounds0.0590.011-0.008-0.095-0.023-0.0330.0010.062-0.029NaN-0.004-0.104-0.041-0.0490.0550.025-0.019-0.0250.081-0.0470.0090.0441.000-0.0710.0440.0111.0000.131-0.071
weight_pounds0.1000.0070.1000.052-0.0200.002-0.0940.106-0.040-0.0020.0230.041-0.031-0.1570.3790.110-0.0070.0870.118-0.1460.0400.1830.020-0.0180.083-0.0080.1311.000-0.018
year0.0830.637-0.0880.019-0.0510.0690.6750.006-0.1020.004-0.269-0.0060.0090.029-0.0560.001-0.0040.0720.093-0.0350.1590.0120.9331.0000.316-0.001-0.071-0.0181.000

Missing values

2024-07-30T18:02:23.002264image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
A simple visualization of nullity by column.
2024-07-30T18:02:24.140564image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-07-30T18:02:25.708957image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

source_yearyearmonthdaywdaystateis_malechild_raceweight_poundspluralityapgar_1minapgar_5minmother_residence_statemother_racemother_agegestation_weekslmpmother_marriedmother_birth_statecigarette_usecigarettes_per_dayalcohol_usedrinks_per_weekweight_gain_poundsborn_alive_aliveborn_alive_deadborn_deadever_bornfather_racefather_agerecord_weight
0200520051NaN3.0NaNTrueNaN7.8043641.0NaNNaNNaN38.03040.04172004TrueNaNNaNNaNNaNNaN99.05.02.01.08.038.0241
1200520052NaN7.0NaNFalseNaN5.3748702.0NaN8.0NaN1.02938.05992004TrueNaNFalseNaNFalseNaN8.09.00.00.010.01.0311
2200520055NaN4.0NaNFalseNaN6.4374981.0NaN7.0NaNNaN2439.099999999TrueNaNNaNNaNNaNNaN99.0NaNNaNNaNNaNNaN311
3200520058NaN4.0NaNFalseNaN6.5609571.0NaN9.0NaNNaN26NaN99999999FalseNaNNaNNaNNaNNaN25.0NaNNaNNaNNaNNaN991
4200520055NaN6.0NaNTrueNaN8.8118771.0NaN9.0NaNNaN2240.099999999FalseNaNNaNNaNNaNNaN99.0NaNNaNNaNNaNNaN331
5200520055NaN4.0NaNFalseNaN6.1244421.0NaNNaNNaN7.02839.08112004TrueNaNNaNNaNNaNNaN99.00.00.00.01.07.0331
62005200510NaN6.0NaNTrueNaN6.8762181.0NaN9.0NaNNaN3040.01112005TrueNaNNaNNaNNaNNaN25.00.00.01.01.0NaN501
72005200510NaN2.0NaNTrueNaN6.7571681.0NaN9.0NaNNaN1938.01102005FalseNaNNaNNaNNaNNaN25.00.00.00.01.0NaN241
82005200510NaN2.0NaNFalseNaN8.6884181.0NaN9.0NaNNaN2739.01192005FalseNaNNaNNaNNaNNaN47.00.00.00.01.0NaN301
9200520059NaN6.0NaNFalseNaN6.9996771.0NaN9.0NaNNaN2040.012142004FalseNaNNaNNaNNaNNaN42.00.00.00.01.0NaN301
source_yearyearmonthdaywdaystateis_malechild_raceweight_poundspluralityapgar_1minapgar_5minmother_residence_statemother_racemother_agegestation_weekslmpmother_marriedmother_birth_statecigarette_usecigarettes_per_dayalcohol_usedrinks_per_weekweight_gain_poundsborn_alive_aliveborn_alive_deadborn_deadever_bornfather_racefather_agerecord_weight
13999019701970321.0NaNARFalse1.07.187070NaNNaNNaNAR1.028NaN88881908TrueARNaNNaNNaNNaNNaN3.00.01.05.01.0312
13999119701970915.0NaNARFalse1.06.937947NaNNaNNaNTN1.023NaN88881908TrueTNNaNNaNNaNNaNNaN3.00.00.04.01.0332
13999219701970619.0NaNARTrue1.07.500126NaNNaNNaNAR1.042NaN88881908TrueForeignNaNNaNNaNNaNNaN3.00.00.04.01.0472
13999319701970727.0NaNARTrue2.06.686620NaNNaNNaNAR2.028NaN88881908TrueARNaNNaNNaNNaNNaN6.00.00.07.02.0542
1399941971197193.0NaNARTrue1.08.6244841.0NaNNaNAR1.020NaN88881918TrueCANaNNaNNaNNaNNaN0.00.00.01.01.0212
139995197119711110.0NaNARFalse1.06.3757691.0NaNNaNAR1.019NaN88881918TrueARNaNNaNNaNNaNNaN0.00.00.01.01.0212
139996197119711220.0NaNARFalse1.07.2510041.0NaNNaNAR1.019NaN88881918TrueARNaNNaNNaNNaNNaN0.00.00.01.01.0222
13999719711971614.0NaNARFalse1.06.6248911.0NaNNaNAR1.020NaN88881918TrueARNaNNaNNaNNaNNaN0.00.00.01.01.0262
13999819711971518.0NaNARTrue1.06.4374981.0NaNNaNAR1.018NaN88881918TrueARNaNNaNNaNNaNNaN0.00.00.01.01.0202
13999919711971417.0NaNARTrue1.06.9379471.0NaNNaNAR1.019NaN88881918TrueARNaNNaNNaNNaNNaNNaN0.00.0NaN1.0292